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Incorporating real-time data processing into business workflows can unlock transformative insights, enabling companies to respond instantly to changing conditions and emerging trends. However, building a production-grade stream processing platform with Apache Flink requires more than just the right technology stack—it also requires addressing complex operational and scalability challenges.
Embarking on this journey raises essential questions: What does building a production-ready stream processing platform entail? What considerations should you consider beyond the core technologies? What are the things that turn the promised capabilities into actual line-of-business functionality? At Decodable, we have spent years exploring and addressing the complexities of this challenge, resulting in a comprehensive platform that serves as the foundation that makes real-time processing accessible, powerful, and reliable.
I talked with our team of Flink experts to get their take on some of the most challenging aspects of providing a stream processing platform powered by Flink.
Challenge #1: Creating a comprehensive user experience
When first getting started with Flink in a proof-of-concept environment, the primary goal is simply making it work. Infrastructure is fully accessible to those responsible for the POC, often without a unified user interface. However, scaling stream processing beyond this phase requires a more intuitive way for teams to interact with Flink. Users need visibility into key components like connections, streams, and jobs, along with a structured interface to explore resources, inspect schemas, and track running jobs—eliminating redundant setup and misconfigurations.
As stream processing pipelines grow, understanding end-to-end data flows becomes critical, especially when multiple jobs run in sequence. Dependencies must be carefully managed to avoid feedback loops and inefficiencies. An ideal solution would include interactive lineage graphs to visualize data movement, but designing them is complex. With potentially hundreds of streams, overly detailed graphs can become overwhelming rather than useful. Since Flink lacks built-in job coordination tools, balancing clarity with detail is essential to help users navigate their data flows effectively.
Challenge #2: Building a robust developer experience
Building a robust developer experience around Apache Flink is challenging due to the complexity of stream processing and the need to support multiple programming languages like SQL, Java, and Python. Each language comes with different debugging tools and optimization strategies, making it difficult to maintain a consistent workflow across teams. Developers also require fast, repeatable ways to test and preview job results without long deployment cycles or resource-heavy infrastructure, ensuring efficient iteration and debugging.
Debugging Flink jobs is particularly difficult because failures can stem from various sources, including upstream data issues, misconfigured connections, or infrastructure problems unrelated to the job itself. Developers need clear visibility into dependencies to quickly identify root causes, making observability tools like detailed logging, real-time metrics, and lineage tracking essential. Additionally, a structured workflow for moving from development to production—including source control integration, automated validation, and CI/CD support—is critical for ensuring reliability and minimizing inconsistencies across environments.
Challenge #3: Implementing efficient, reliable connectors
Developing Flink connectors is highly challenging, as open-source options can be inefficient, not fully reliable, or missing key features needed for production workloads. Fixing these issues requires deep engineering expertise to modify code written by various contributors, while building a custom connector from scratch demands an even greater understanding of both Flink and the external data system. System integration is complex, and achieving stability takes time, with connectors only becoming reliable through iterative improvements. As a result, organizations must be prepared for a lengthy development and stabilization process, making connector development one of the most resource-intensive aspects of a Flink-based architecture.
We have created an entire technical guide to explore all the facets of Flink connectors and Debezium for change data capture (CDC), as well as their role within a larger data platform. It delves into considerations beyond mere implementation, including common challenges and how Decodable's fully-managed connectors alleviate these burdens. Check it out to learn about:
- The available open-source connectors to use as a starting point
- The difficulties of ensuring security and compliance with numerous connectors and jobs
- The requirements for managing and maintaining connectors
- The complexity involved in creating a connector from scratch
- How Decodable abstracts away integration challenges
Challenge #4: Establishing effective resource management
Effective resource management in Apache Flink requires a structured approach to organizing the growing number of streams, connectors, and processing jobs. A comprehensive data catalog is essential for tracking and managing assets across teams, preventing inefficiencies and redundant configurations. A unified API ensures consistency in querying, modifying, and monitoring resources, while declarative resource management enables users to define desired states in source control. This allows for versioning, automated validation, and a reconciliation process that ensures resources align with expected configurations. Resource tagging further helps teams categorize assets based on ownership, environment, or purpose.
Pipeline lifecycle management is also critical for maintaining stability when adding, removing, starting, or stopping connectors, streams, and processing jobs. Dependencies between jobs and connectors complicate changes, as stopping a job could break downstream consumers or adding a stream might require careful schema validation. Effective coordination across teams necessitates role-based access controls, audit trails, and automated validation to prevent disruptions. A well-structured resource management approach ensures a scalable and maintainable Flink ecosystem.
Challenge #5: Managing schema evolution and inference
Schema evolution quickly becomes a challenge in a production Flink environment as streams inevitably change. Organizations need systems that support automatic schema inference and updates while allowing manual intervention when necessary. Enforcing schema contracts prevents unexpected mismatches, ensuring downstream systems can reliably process data without failures or inconsistencies. Without proper enforcement, consumers may struggle with incompatible data, leading to processing errors and disruptions.
Detecting and propagating schema changes while assessing their impact on dependencies is another critical challenge. Some modifications may be backward-compatible, requiring no changes from consumers, while others necessitate full state resets or reloads. The approach depends on how frequently schemas change and the associated costs of rebuilding state. Given these complexities, managing schema evolution effectively requires a combination of real-time validation, proactive change detection, compatibility checks, and a clear governance framework.
Challenge #6: Providing actionable error handling
Providing actionable error handling in Flink is challenging because errors can originate from multiple layers, including Kubernetes, the Flink engine, connectors, or processing jobs. Navigating error traces requires expertise, as raw logs often mix useful signals with misleading red herrings, making it difficult to pinpoint the root cause. Without structured error classification, developers may waste time chasing symptoms rather than addressing the actual issue.
Understanding how errors propagate across dependencies is crucial for effective debugging. A Kubernetes failure can create a cascading effect, making all jobs appear broken, while a connector issue might disrupt downstream processing. Similarly, a failing upstream job can cause dependent jobs to fail, leading to misdiagnoses. To address this, platforms need to expose relevant errors to the right people and provide tools like interactive lineage graphs to visualize dependencies, track failures, and accelerate issue resolution, ensuring a more resilient Flink platform.
Challenge #7: Implementing security and compliance controls
Implementing security and compliance controls for an Apache Flink platform is complex due to the need to restrict access to both Flink resources and underlying infrastructure like AWS, Kubernetes, and the Flink engine itself. Unlike traditional applications with neatly segmented access, Flink operates across multiple layers, requiring strict boundaries on what users can modify. A robust user management system with authentication is essential to ensure only verified users interact with the platform. Managing permissions across components is further complicated by open-source connectors that assume seamless access, necessitating careful configuration to prevent security risks.
Role-based access control (RBAC) is crucial for enforcing granular permissions, allowing users to see relevant resources and upstream dependencies without modifying them. Organizations must balance security and usability, ensuring sensitive resources remain protected while still allowing efficient collaboration. Compliance with regulations like SOC 2, GDPR, and HIPAA adds another layer of complexity, requiring consistent enforcement of security policies across teams. Given that Flink experts may not be familiar with cloud security policies and vice versa, successful implementation requires not just technical solutions but also governance frameworks and collaboration between security, infrastructure, and data engineering teams.
Challenge #8: Integrating monitoring and observability systems
Integrating monitoring and observability into an Apache Flink platform can be daunting, requiring organizations to determine how to ingest Flink-specific metrics into existing systems like Datadog, Splunk, or Prometheus. Custom instrumentation is necessary to expose relevant logs, metrics, and traces, but knowing which metrics matter and how to interpret them effectively takes time and expertise. Since Flink is a real-time, stateful system, basic infrastructure monitoring is insufficient—organizations must track job performance, resource utilization, and state growth over time. Without a clear observability strategy, companies may spend years experimenting before arriving at an effective approach.
A major challenge is distinguishing between critical alerts and noise, as not all failures require immediate action. Organizations need visibility into both the high-level platform state and granular details about individual connectors, streams, and jobs. Different workloads have unique monitoring needs, making a one-size-fits-all alerting strategy ineffective. Even with logs and metrics, diagnosing performance bottlenecks or resource inefficiencies requires deep operational experience. Learning to optimize workloads—ensuring they are right-sized without excessive latency or cost—only comes with long-term production use. Companies that define a clear observability strategy early can avoid years of costly trial and error while improving system reliability.
Challenge #9: Providing ongoing maintenance for the entire platform
Maintaining a Flink stream processing platform is an ongoing challenge, requiring constant updates across multiple layers of the stack. Security vulnerabilities in Flink, Kubernetes, storage layers like Kafka, and third-party connectors necessitate frequent version bumps, while functionality and performance improvements drive additional upgrades. Organizations must continuously evaluate, test, and roll out new versions to leverage new features while ensuring backward compatibility. Supporting multiple Flink versions for different workloads adds further complexity, increasing operational overhead and requiring careful planning to prevent regressions.
Beyond Flink itself, maintaining the broader infrastructure is equally demanding. Kubernetes upgrades can introduce breaking changes, Kafka updates may impact data retention or message semantics, and connectors require ongoing fixes and optimizations. Internal Flink jobs also need continuous monitoring and updates to ensure efficiency. The biggest challenge is managing interdependencies, where a single upgrade can have cascading effects across jobs and systems. Thorough testing, rollback strategies, and clear communication with teams are essential to avoid unexpected failures. Without a structured maintenance strategy, organizations risk accumulating technical debt, reducing platform stability and scalability over time.
Challenge #10: Mastering the open-source ecosystem
Mastering the Flink open-source ecosystem is challenging because Flink itself is just the processing engine—everything else required for running it at scale is left to the user. Unlike fully managed services, Flink deployments demand decisions on orchestration, configuration, and infrastructure management. Kubernetes is the default choice for scalability, but tuning and maintaining it is complex. Beyond that, teams must select logging, monitoring, and orchestration tools from an overwhelming array of open-source options, each with its own trade-offs. Navigating this evolving landscape requires deep expertise and constant effort to stay ahead of best practices.
The challenge is further compounded by the rapid evolution of supporting projects like storage layers, monitoring solutions, and security frameworks. Organizations must continuously track which technologies are gaining traction and when to replace outdated dependencies. However, swapping components can disrupt entire architectures, leading to costly migrations. This ongoing research burden often leads to decision fatigue, as teams struggle to find the "best" combination of tools. While flexibility is valuable, most companies would benefit from a more prescriptive approach that lets them focus on business logic instead of infrastructure concerns. Without strong guidance or a managed solution, teams risk being consumed by endless configuration, integration, and maintenance work just to keep their Flink environment stable.
Conclusion
Running Apache Flink in production is no small feat. It requires significant expertise, infrastructure, and ongoing maintenance. Decodable provides a comprehensive, fully-managed platform for stream processing with Apache Flink, enabling businesses to rapidly build and deploy real-time applications at scale. By abstracting infrastructure complexities and providing extensive connectivity, schema management, SQL support, automated scaling, built-in observability, and expert support, Decodable empowers data teams to focus on delivering business value through real-time data processing.
Decodable's platform is designed to accelerate the journey from proof-of-concept to production-readiness, without the need to build entire infrastructures from the ground up. With Decodable, businesses can quickly and easily gain the benefits of real-time ETL/ELT and stream processing, leveraging the power of Apache Flink and Debezium.
